def contours(table, params):
extractor = DBExtractor(table)
for par_limit, contour_param in zip(["gamma", "rv"], ["rv", "gamma"]):
fixed_params = {"teff_1": params["teff"], "logg_1": params["logg"], "feh_1": params["fe_h"],
"logg_2": params["logg"], "feh_2": params["fe_h"]}
for chi2_val, npix_val in zip(chi2_names, npix_names):
df_min_chi2 = extractor.minimum_value_of(chi2_val)
fixed_params.update({par_limit: float(df_min_chi2[par_limit][0])})
df = extractor.fixed_extraction(columns=["teff_2", "rv", "gamma", chi2_val],
fixed=fixed_params)
params["this_npix"] = params["npix"][npix_val]
params["par_limit"] = par_limit
pars = [contour_param, "teff_2", chi2_val]
dataframe_contour(df, xcol=pars[0], ycol=pars[1], zcol=pars[2], params=params)
# Using Reduced chi2 value
for par_limit, contour_param in zip(["gamma", "rv"], ["rv", "gamma"]):
fixed_params = {"teff_1": params["teff"], "logg_1": params["logg"], "feh_1": params["fe_h"],
"logg_2": params["logg"], "feh_2": params["fe_h"]}
for chi2_val, npix_val in zip(chi2_names, npix_names):
red_chi2 = "red_{0}".format(chi2_val)
df_min_chi2 = extractor.minimum_value_of(chi2_val)
fixed_params.update({par_limit: float(df_min_chi2[par_limit][0])})
df = extractor.fixed_extraction(columns=["teff_2", "rv", "gamma", chi2_val],
fixed=fixed_params)
df[red_chi2] = reduced_chi_squared(df[chi2_val], params["npix"][npix_val], params["npars"])
params["this_npix"] = params["npix"][npix_val]
params["par_limit"] = par_limit
pars = [contour_param, "teff_2", red_chi2]
dataframe_contour(df, xcol=pars[0], ycol=pars[1], zcol=pars[2], params=params)
# Using teff_1 contour
for par_limit, contour_param in zip(["gamma", "rv"], ["rv", "gamma"]):
fixed_params = {"logg_1": params["logg"], "feh_1": params["fe_h"],
"logg_2": params["logg"], "feh_2": params["fe_h"]}
for chi2_val, npix_val in zip(chi2_names, npix_names):
df_min_chi2 = extractor.minimum_value_of(chi2_val)
fixed_params.update({par_limit: df_min_chi2[par_limit].values[0],
"teff_2": df_min_chi2["teff_2"].values[0]})
df = extractor.fixed_extraction(columns=["teff_1", "rv", "gamma", chi2_val],
fixed=fixed_params)
params["this_npix"] = params["npix"][npix_val]
params["par_limit"] = par_limit
pars = [contour_param, "teff_1", chi2_val]
dataframe_contour(df, xcol=pars[0], ycol=pars[1], zcol=pars[2], params=params)
def contrast_iam_results(table, params):
extractor = DBExtractor(table)
star_name = params["star"]
obsnum = params["obsnum"]
___, host_params, ___ = iam_helper_function(star_name, obsnum, 1)
h_temp, h_logg, h_feh = host_params['temp'], host_params['logg'], host_params["fe_h"]
c_temp = host_params.get("comp_temp")
print(
"Observation {4} - {5}\n"
"Expected Parameters\n---------------------\n"
"teff={0:5.0f} logg={1:3.02f} feh={2:4.01f} \tcompanion_temp={3:5.0f} ".format(h_temp, h_logg, h_feh, c_temp,
star_name, obsnum))
print("IAM SOLUTIONS\n---------------------")
for ii, chi2_val in enumerate(chi2_names):
df = extractor.minimum_value_of(chi2_val)
print(
"{0:10} solution:\nCompanion: teff_2={1:5.0f} logg2={2:4.02f} ".format(chi2_val, df.teff_2.values[0],
df.logg_2.values[0]) +
"feh2={0:4.01f} gamma={1:4.01f} rv={2:4.01f} ".format(df.feh_2.values[0], float(df.gamma.values[0]),
float(df.rv.values[0])) +
"Host: teff={0:5.0f} logg={1:4.02f} feh={2:4.01f} chi2={3:8.02f} median alpha={4:5.03f}".format(
df.teff_1.values[0],
df.logg_1.values[0],
df.feh_1.values[0],
df[chi2_val].values[0],
np.median([df["alpha_1"].values[0],
df["alpha_2"].values[0],
df["alpha_3"].values[0],
df["alpha_4"].values[0]])))
def contours(table, params):
extractor = DBExtractor(table)
for chi2_val, npix_val in zip(chi2_names, npix_names):
df_min_chi2 = extractor.minimum_value_of(chi2_val)
fixed_params = {
"logg_1": float(df_min_chi2["logg_1"].values[0]),
"feh_1": float(df_min_chi2["feh_1"].values[0])
}
df = extractor.fixed_extraction(
columns=["teff_1", "feh_1", "logg_1", "gamma", chi2_val],
fixed=fixed_params)
params["this_npix"] = params["npix"][npix_val]
params["chi2_value"] = chi2_val
pars = ["gamma", "teff_1", chi2_val]
dataframe_contour(df,
xcol=pars[0],
ycol=pars[1],
zcol=pars[2],
params=params)
# Reduced chi2
for chi2_val, npix_val in zip(chi2_names, npix_names):
red_chi2 = "red_{0}".format(chi2_val)
df_min_chi2 = extractor.minimum_value_of(chi2_val)
fixed_params = {
"logg_1": float(df_min_chi2["logg_1"].values[0]),
"feh_1": float(df_min_chi2["feh_1"].values[0])
}
df = extractor.fixed_extraction(
columns=["teff_1", "feh_1", "logg_1", "gamma", chi2_val],
fixed=fixed_params)
df[red_chi2] = reduced_chi_squared(df[chi2_val],
params["npix"][npix_val],
params["npars"])
params["this_npix"] = params["npix"][npix_val]
params["chi2_value"] = chi2_val
pars = ["gamma", "teff_1", red_chi2]
dataframe_contour(df,
xcol=pars[0],
ycol=pars[1],
zcol=pars[2],
params=params)
def contrast_bhm_results(table, params):
extractor = DBExtractor(table)
star_name = params["star"]
obsnum = params["obsnum"]
___, host_params, ___ = bhm_helper_function(star_name, obsnum, 1)
h_temp, h_logg, h_feh = host_params['temp'], host_params[
'logg'], host_params["fe_h"]
print(
"Expected Parameters\n---------------------\nteff={0}\tlogg={1}\tfeh={2}"
.format(h_temp, h_logg, h_feh))
print("BHM SOLUTIONS\n---------------------")
for ii, chi2_val in enumerate(chi2_names):
df = extractor.minimum_value_of(chi2_val)
print("{0}: teff={1:5}\tlogg={2:3.02}\t".format(
chi2_val, df.teff_1.values[0], df.logg_1.values[0]) +
"feh={0:4.1}\tgamma={1:3.2},\txcorr={2:3.2},\tchi2={3:8.03}".
format(df.feh_1.values[0], float(df.gamma.values[0]),
df.xcorr_1.values[0], df[chi2_val].values[0]))
def compare_spectra(table, params):
"""Plot the min chi2 result against the observations."""
extractor = DBExtractor(table)
for ii, chi2_val in enumerate(chi2_names[0:-2]):
df = extractor.minimum_value_of(chi2_val)
df = df[["teff_1", "logg_1", "feh_1", "gamma",
"teff_2", "logg_2", "feh_2", "rv", chi2_val]]
params1 = [df["teff_1"].values[0], df["logg_1"].values[0], df["feh_1"].values[0]]
params2 = [df["teff_2"].values[0], df["logg_2"].values[0], df["feh_2"].values[0]]
params1 = [float(param1) for param1 in params1]
params2 = [float(param2) for param2 in params2]
gamma = df["gamma"].values
rv = df["rv"].values
from simulators.iam_module import iam_helper_function
obs_name, obs_params, output_prefix = iam_helper_function(params["star"], params["obsnum"], ii + 1)
obs_spec = load_spectrum(obs_name)
# Mask out bad portion of observed spectra
# obs_spec = spectrum_masking(obs_spec, params["star"], params["obsnum"], ii + 1)
# Barycentric correct spectrum
_obs_spec = barycorr_crires_spectrum(obs_spec, extra_offset=None)
normalization_limits = [obs_spec.xaxis[0] - 5, obs_spec.xaxis[-1] + 5]
# models
# print("params for models", params1, params2)
mod1 = load_starfish_spectrum(params1, limits=normalization_limits,
hdr=True, normalize=False, area_scale=True,
flux_rescale=True)
mod2 = load_starfish_spectrum(params2, limits=normalization_limits,
hdr=True, normalize=False, area_scale=True,
flux_rescale=True)
iam_grid_func = inherent_alpha_model(mod1.xaxis, mod1.flux, mod2.flux,
rvs=rv, gammas=gamma)
iam_grid_model = iam_grid_func(obs_spec.xaxis).squeeze()
iam_grid_model_full = iam_grid_func(mod1.xaxis).squeeze()
model_spec_full = Spectrum(flux=iam_grid_model_full, xaxis=mod1.xaxis)
model_spec = Spectrum(flux=iam_grid_model, xaxis=obs_spec.xaxis)
model_spec = model_spec.remove_nans()
model_spec = model_spec.normalize(method="exponential")
model_spec_full = model_spec_full.remove_nans()
model_spec_full = model_spec_full.normalize(method="exponential")
fig, ax = plt.subplots(1, 1)
plt.plot(obs_spec.xaxis, obs_spec.flux, label="Observation")
plt.plot(model_spec.xaxis, model_spec.flux, label="Minimum \chi^2 model")
plt.plot(model_spec_full.xaxis, model_spec_full.flux, "--", label="Model_full_res")
plt.legend()
fig.tight_layout()
name = "{0}-{1}_{2}_{3}_min_chi2_spectrum_comparison_{4}.png".format(
params["star"], params["obsnum"], params["chip"], chi2_val, params["suffix"])
plt.savefig(os.path.join(params["path"], "plots", name))
plt.close()
plt.plot(obs_spec.xaxis, obs_spec.flux, label="Observation")
plt.show()